Analytics 'R' Us

By heading up Revolution Analytics, Norman Nie is putting himself in competition with SPSS, a company he himself started decades ago. Revolution Analytics -- previously known as "Revolution Computing" -- aims to take the open source R language, which specializes in statistical computing, into the business world and make it an analytics powerhouse.

By Jack M. Germain
06/11/10 5:00 AM PT

A growing recognition of the business benefits predictive analysis
provides is positioning newcomer Revolution Analytics into a key role to help adopters of the
R programing language migrate from legacy offerings. Until a recent
funding infusion and a refocus of marketing goals, the startup did
business under the name "Revolution Computing."

Revolution Analytics, led by predictive-analytics expert Norman
Nie, recently released an alpha version of its commercial product
based on the open source language known as "R." The company markets
enhanced versions called "Revolution R" and "Revolution R Enterprise."

The new company, which launched on May 4, is headed by CEO Norman Nie.
In fact, some 40 years ago, Nie founded SPSS (Statistical Package
for the Social Sciences). That company's mission was to spearhead the
widespread use of data in decision-making. Nie was one of three people who developed a software system based on R and the
idea of using statistics to turn raw data into information essential
to the decision-making process.

In his new corporate role, Nie is aiming to disrupt the market he
helped create and take on his old company head-to-head using the
technical and cost-advantages of open source R.

When funding partners approached him about taking over the new company
and further developing R, Nie's first reaction was, "No way. I'm not
doing this again," he told Linuxinsider. Then he gave the plan more
thought.

"I started thinking about what it would take. I began to understand
the opportunities and the limits of the GPL (General Public License).
All of a sudden the picture occurred to me of an unbelievable software
analytics company that could handle the biggest data files and would
have the speed to overtake the legacy companies," he said.

The R Factor

R is a language and environment for statistical computing and
graphics. The open source GNU project software project runs on a wide
variety of Unix/Linux platforms, Windows and the MacOS.

R provides a wide variety of statistical modeling and graphical
techniques. It is highly extensible. A key strength is its ability to
produce well-designed, publication-quality plots, including
mathematical symbols and formulas.

R is the choice of statisticians, Nie explained.

"There are no statistical expressions that can not be written in R," he said.

Predictable Outcomes

Revolution Analytics is working to be the rallying point for the
commercial development of R. Nie sees a good chance for the company to
become for R in the predictive analysis space what RedHat is to the
Linux community.

To that end, the company is spending money to focus the open source
community around its website, he said. The commercial product will be
free to academics under a dual license.

"We want to take advantage of the use of R and put our IP
(intellectual property) on it for commercial adoption," he said.

Academic Stronghold

In essence, Revolution Analytics is putting its money on an academic
infrastructure of R users to earn a return from the commercial users.
The R language has its genesis among university researchers and
graduate students.

That once-academic fervor for statistical analysis is moving into the
business world. A new generation of college-trained statisticians will
bring their programming skills in R with them. Why? Because it is so
popular in Academia with researchers.

"So that has the potential to spur its adoption. Its use in
academia can be an appealing factor," David White, senior researcher
analyst for the Aberdeen Group, told LinuxInsider.

R Gets A+

The company has made some significant improvements in the
community-developed version, White said. For example, it is now much
easier to use analytics. The improvements address a key area where
users lack key skills. They don't need a PhD to use it.

That sort of improvement comes on top of the existing benefits of
using the R language. That is the cutting edge advantage the company
brings to market.

"Nothing specifically like R has ever been developed for research
analysis. It is the only language ever developed by statisticians,"
Jeff Erhardt, COO of Revolution Analytics, told Linux Insider.

Other programming languages such as C and Python do not have the same
level of productivity. Over the last five years, use of R has literally
taken over the academic world, he said.

Mission Overhaul

Known as "Revolution Computing" when it opened for business in 2007, the
company received $9 million in Series B funding from North Bridge
Venture Partners and Intel Capital in October 2009. That's when Nie
took over as CEO.

Despite the company's unique position of being in the forefront of the
R evolution, the investors wanted a change in direction to better
confront the marketplace. The predictive analysis space was dominated
by only a few companies, noted Nie.

The launch of the alpha version of the enterprise version last month
came with the announcement of a corporate name change.

Prior to the new round of financing and Norman Nie joining, Revolution
was focused on commercializing R, but not specifically for predictive
analytics use. Their primary efforts as Revolution Computing were
focused on bringing parallel computing technology to R, as opposed to
a focus on the broad analytics market, COO Erhardt explained.

Namesake Strategy

"A change is going on in the industry. Previously, proprietary firms
such as SAS and SPSS dominated the field. Revolution Computing is
taking a new tack with its open source software development," Dave Stodder, principal analyst for Perceptive Information Strategies, told LinuxInsider about the company prior to its name change.

The recent corporate name upgrade may mirror a maturing of the concept
behind predictive analytics. The software field for predictive
analytics is wide open as it is relatively
new, noted the Aberdeen Group's White.

What the company is doing is driving down the cost of using analytical
tools. This allows software developers to include analytics in their
applications, added White.

"This new field of predictive analytics is really just a variant of
what was statistical modeling. R just sits on that wave. With the kind
of corporate backing we're providing, I think it is an incredible
opportunity. I see great disruptive innovations," Nie said of his
company's viability for success.

Stumbling Block

Nie faces a major sticking point in pushing the commercial version of
R against legacy options. For many old-school businesses, open source
software is banned.

"A challenge to the adoption of R is its open source roots. A lot of
enterprises shy away from open source software because of its
potential for not being supported," said White.

For example, a representative from a large insurance firm looking at R
told him that basically hell would freeze over before open source ever
was used in that company.

Then and Now

Another obstacle that Nie faces is the different marketing strategy he faces.
When he first marketed the R concept, no incumbents existed. Today he
must contend with SAP and his old company, SPSS, which is now a part
of IBM.

"So part of our focus is educating the market about the Revolutionary
process. Our pitch is to show the scalability and usability compared
to these other solutions. All of the innovation in predictive analysis
is coming from open source," he said.

Core Goals for Development

Navigating the newly renamed company through a growing predictive
analytics space is much like facing a perfect storm, suggested Nie.

You have all these graduates trained in using R, plus a
growing demand in business for statistical analysis, coupled with
legacy solutions that are very costly.

The startup may already have a head start. To push forward, Nie is
following a three-part plan.

One, he has to scale R so it can be used in enterprise applications.
Two, he has to build in greater usability. Three, he
needs to develop migration tools to enable people to move all their
legacy products into R.